onnx-source-snapshot/examples/make_model.ipynb

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# NBVAL_SKIP
# Protobuf 4 and Protobuf 3 might output different order of protobuf fields

import onnx
from onnx import helper
from onnx import AttributeProto, TensorProto, GraphProto


# The protobuf definition can be found here:
# https://github.com/onnx/onnx/blob/main/onnx/onnx.proto


# Create one input (ValueInfoProto)
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])

# Create second input (ValueInfoProto)
Pads = helper.make_tensor_value_info("Pads", TensorProto.INT64, [4])

# Create one output (ValueInfoProto)
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 4])

# Create a node (NodeProto)
node_def = helper.make_node(
    "Pad", # node name
    ["X", "Pads"], # inputs
    ["Y"], # outputs
    mode="constant", # Attributes
)

# Create the graph (GraphProto)
graph_def = helper.make_graph(
    [node_def],
    "test-model",
    [X, Pads],
    [Y],
    [helper.make_tensor("Pads", TensorProto.INT64, [4,], [0, 0, 1, 1,])],
)

# Create the model (ModelProto)
model_def = helper.make_model(graph_def,
                              producer_name="onnx-example")

print("The producer_name in model: {}\n".format(model_def.producer_name))
print("The graph in model:\n{}".format(model_def.graph))
onnx.checker.check_model(model_def)
print("The model is checked!")

The producer_name in model: onnx-example

The graph in model:
node {
  input: "X"
  input: "Pads"
  output: "Y"
  op_type: "Pad"
  attribute {
    name: "mode"
    s: "constant"
    type: STRING
  }
}
name: "test-model"
initializer {
  dims: 4
  data_type: 7
  int64_data: 0
  int64_data: 0
  int64_data: 1
  int64_data: 1
  name: "Pads"
}
input {
  name: "X"
  type {
    tensor_type {
      elem_type: 1
      shape {
        dim {
          dim_value: 1
        }
        dim {
          dim_value: 2
        }
      }
    }
  }
}
input {
  name: "Pads"
  type {
    tensor_type {
      elem_type: 7
      shape {
        dim {
          dim_value: 4
        }
      }
    }
  }
}
output {
  name: "Y"
  type {
    tensor_type {
      elem_type: 1
      shape {
        dim {
          dim_value: 1
        }
        dim {
          dim_value: 4
        }
      }
    }
  }
}

The model is checked!

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